Incidents cost money, customers, and happiness. One way to reduce incidents is to build a robust enterprise change management process that can promote responsible change management behavior among application teams. A change management program can satisfy regulatory requirements and prevent high severity incidents. The migration to the computing cloud environment has created a challenge to effectively monitor change deployment behavior. Cloud computing environment audit logs enable event level monitoring, but these events are too granular to easily map to application changes. It is possible to monitor and produce change clusters from event data using business rules rather than a machine learning algorithm. However, a rules-based approach would have certain limitations: rules would need to be regularly maintained and updated by a person, and the rules may require significant changes if applied to slightly different data.
Aspects described herein may address these and other problems, and generally improve the quality, efficiency, and speed of monitoring change activity in a cloud computing environment using a machine learning algorithm by providing a near real-time detective control to identify any potential unauthorized changes within the cloud computing environment, using the machine learning algorithm to group change events into change clusters, and sending an automated message or notification to the change implementer and the application team that directs them to enter information about the change activity.